Washington: Computer scientists have developed a new web application that can help researchers and clinicians explore the genetic underpinnings of cancer.

The tool – Mutation Annotation and Genome Interpretation (MAGI) – is a free open-source web application that enables users to search, visualise, and annotate large public cancer genetics datasets, including data from The Cancer Genome Atlas (TCGA) project, which catalogues genetic mutations responsible for cancer, using genome sequencing and bioinformatics. “The main motivation for MAGI has been to reduce the computational burden required for researchers or doctors to explore and annotate cancer genomics data,” said Max Leiserson, a PhD student at Brown University. “MAGI lets users explore these data in a regular web browser and with no computational expertise required,” said Leiserson, who led the development of the tool.

In addition to viewing TCGA data, the portal also allows users to upload their own data and compare their findings to those in the larger databases. “Over the last decade, researchers working with TCGA have sequenced genes from thousands of tumours and dozens of cancer types in an effort to understand which mutations contribute to the development of cancer,” said Ben Raphael, director of Brown University’s Centre for Computational and Molecular Biology, who helped oversee the project.

“At the same time, as sequencing has gotten faster and cheaper, individual researchers have begun sequencing samples from their own studies, sometimes from just a few tumours,” Raphael said.

By uploading their data to MAGI, researchers can leverage the large public datasets to help interpret their own data. “In cancer genomics, there’s real value in large sample sizes because mutations are diverse and spread all over the genome,” Raphael said. MAGI has data from TCGA already loaded. Users can search by cancer type, by individual genes, or by groups of genes. The output offers several ways of visualising the search results, showing how often a given gene is mutated across samples and what types of mutations they were.

“When someone uploads data to MAGI, they can use the public data to help them interpret their own dataset,” Raphael said.

“But in the process, they might also be able say something about the public data. We thought – wouldn’t it be great if users could record that information and share it?” Raphael said.

The MAGI project started as a means of looking at the output from algorithms that Raphael’s lab develops.

Those algorithms comb through large genome datasets, helping to pick out the mutations that are important to cancer development and distinguishing them from benign mutations that are just along for the ride.

“As we were developing tools to visualise our own results, we realised that other researchers might also find these tools useful,” Raphael said.

“We decided to develop a public portal for the cancer genomics research community,” Raphael said.